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利用地理空间建模绘制全球食用野生动物活动图以改善人畜共患病溢出物监测。

Mapping Global Bushmeat Activities to Improve Zoonotic Spillover Surveillance by Using Geospatial Modeling.

出版信息

Emerg Infect Dis. 2023 Apr;29(4):742-750. doi: 10.3201/eid2904.221022.

Abstract

Human populations that hunt, butcher, and sell bushmeat (bushmeat activities) are at increased risk for zoonotic pathogen spillover. Despite associations with global epidemics of severe illnesses, such as Ebola and mpox, quantitative assessments of bushmeat activities are lacking. However, such assessments could help prioritize pandemic prevention and preparedness efforts. We used geospatial models that combined published data on bushmeat activities and ecologic and demographic drivers to map the distribution of bushmeat activities in rural regions globally. The resulting map had high predictive capacity for bushmeat activities (true skill statistic = 0.94). The model showed that mammal species richness and deforestation were principal drivers of the geographic distribution of bushmeat activities and that countries in West and Central Africa had the highest proportion of land area associated with bushmeat activities. These findings could help prioritize future surveillance of bushmeat activities and forecast emerging zoonoses at a global scale.

摘要

狩猎、屠宰和销售野味(野味活动)的人群感染人畜共患病病原体溢出的风险增加。尽管野味活动与埃博拉和猴痘等严重疾病的全球流行有关,但缺乏对野味活动的定量评估。然而,此类评估可以帮助确定预防和准备大流行病的优先事项。我们使用了地理空间模型,该模型结合了有关野味活动以及生态和人口统计学驱动因素的已发表数据,以绘制全球农村地区野味活动的分布情况。该地图对野味活动具有很高的预测能力(真实技能统计量= 0.94)。该模型表明,哺乳动物物种丰富度和森林砍伐是野味活动地理分布的主要驱动因素,西非和中非国家与野味活动相关的土地面积比例最高。这些发现可以帮助确定未来对野味活动进行监测的重点,并在全球范围内预测新出现的人畜共患传染病。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcf2/10045693/1fd9c08dcf46/22-1022-F1.jpg

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